Geospatial big data for urban planning and urban management

Wu, Huayi; Gui, Zhipeng; Yang, Zelong

doi:10.1080/10095020.2020.1854981

Cited by 25 publications

(15 citation statements)

References 11 publications

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“…(4) The querying range Q consists of area covered by A (A ≥ 1) complete clusters and B (B ≥ 1) intersecting clusters, as depicted in Figure 10d. For the complete clusters falling in the querying range Q, each cluster can provide differential privacy protection similar to use case (2). For the clusters intersected with the querying range Q, the intersected region conforms to use case (3) and provide differential privacy protection.…”

Section: Statistical Publishing and Differential Privacy Protection A...mentioning

confidence: 99%

“…With the rapid proliferation of emerging technologies such as Mobile Internet and the Internet of Things, numerous location-based big data services are becoming increasingly popular in various fields, including, but not limited to, population distribution statistics [1], urban planning and management [2], intelligent traffic scheduling [3], and disease epidemic control [4]. Particularly, in the current critical period of COVID-19 pandemic, a series of location-based big data applications (i.e., health codes, communication itinerary cards, and close contact self-examination procedures) provide great convenience for epidemic prevention and control in terms of epidemiological investigations, epidemic distribution and statistical analysis, personnel and material scheduling, etc.…”

Section: Introductionmentioning

confidence: 99%

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Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

Yan

Sun

Mahmood

et al. 2022

IJGI

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Statistical partitioning and publishing is commonly used in location-based big data services to address queries such as the number of points of interest, available vehicles, traffic flows, infected patients, etc., within a certain range. Adding noise perturbation to the location-based statistical data according to the differential privacy model can reduce various risks caused by location privacy leakage while keeping the statistical characteristics of the published data. The traditional statistical partitioning and publishing methods realize the decomposition and indexing of 2D space from top to bottom. However, they can easily cause the over-partitioning or under-partitioning phenomenon, and therefore need multiple times of data scan. This paper proposes a grid clustering and differential privacy protection method for location-based statistical big data publishing scenarios. We implement location-based big data statistics in units of equal-sized grids and perform density classification on uniformly distributed grids by discrete wavelet transform. A bottom-up grid clustering algorithm is designed to perform on the blank and the uniform grids of the same density level based on neighborhood similarity. The Laplacian noise is incorporated into the clustering results according to the differential privacy model to form the published statistics. Experimental comparison of the real-world datasets manifests that the grid clustering and differential privacy publishing method proposed in this paper is superior to other existing partition publishing methods in terms of range querying accuracy and algorithm operating efficiency.

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Section: Statistical Publishing and Differential Privacy Protection A...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

Yan

Sun

Mahmood

et al. 2022

IJGI

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“…Issues on building a smart sustainable cities are also among those studies [56,57,58]. There are also studies on urbanization strategy and planning [59,60,61], insights for the future of urban ecosystem services research [62].…”

Section: High Dense Classmentioning

confidence: 99%

Mapping and assessment of vegetation cover change and species variation in Medan, North Sumatra

Zaitunah

Samsuri

Sahara

2021

Heliyon

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Vegetation existence contributes to environmental quality in urban areas. The increase in population and development of cities has led to land conversion with lesser vegetated areas. Information on land cover change is needed, especially for urban regional planning with green open space consideration. The research aims to analyze urban vegetation cover and its changes in two sub-districts of Medan between the years 1999 and 2019. Normalized difference vegetation index (NDVI) and change analysis were conducted in the research. The diversity of plants within these areas was observed. The results showed changes in vegetation cover areas in the mentioned years. In 1999, most areas were under a highly dense vegetation class, while in 2019, they were under a lowdensity vegetation class. This finding indicates a decrease in vegetation cover as a result of increasing built-up areas. Within the vegetation cover, it was found many tree species and agricultural plants. Those vegetations existed in some areas: city parks, house yards, gardens, agricultural fields, etc. A special emphasis should be placed on riverside areas with less vegetation in order to provide a higher level of protection, particularly in the event of a flood. To increase the vegetated areas and maintain the environmental quality, optimizing the land by replanting in the area with no or less vegetation should be done.

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“…With over half the population living in urban agglomerations (Hoole, Hincks, and Rae 2019), urban systems pose unique remote sensing challenges. As a branch of remote sensing technology, urban remote sensing takes the city as the observation object (Weng and Quattrochi 2018;Yang 2011Wu, Gui, and Yang 2020;Huang and Wang 2020). Urban fabrics are rather complex, with the existence of various kinds of occlusion from vertical objects such as buildings and trees.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping

Shao

Cheng

et al. 2021

Geo-spatial Information Science

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Geospatial big data for urban planning and urban management

Cited by 25 publications

References 11 publications

Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

Mapping and assessment of vegetation cover change and species variation in Medan, North Sumatra

Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping

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